Crowdsourced informatics for horticultural workflow and exchange

ABSTRACT

Infrastructure and methods to implement a platform for a horticultural operation are disclosed. Sensor data is received from one or more sensors configured to capture data for plants within a plant growth operation. Accumulated data associated with other plants in other plant growth operations is access. The data is analyzed to determine conditions of the plants within the plant growth operation. Plant grower actions to improve plant growth are determined. Instructions are transmitted to a controller device associated with the plant growth operation. Agricultural products or services associated with the plant grower actions are determined. An agricultural exchange service processes electronic commerce information from servicers of the products or services. Bids from the servicers are received, and selection and fulfillment of the bids are facilitated.

BACKGROUND

In order to successfully carry out a horticultural operation,information is collected about the horticultural operation in order toensure successful growth and to identify any problems with theoperation. Modern industrial horticultural operations may involvehundreds or thousands of plants under a plurality of conditions, ingreenhouses or fields, and in different geographic locations and localclimates. Accordingly, collecting the information needed for asuccessful horticultural operation can be difficult and costly.

Additionally, horticultural operations may involve multiple personnelwith different roles in the operation. For example, growers receiverecommendations to use certain agricultural products from a Pest ControlAdviser (PCA) and/or agronomist who is responsible for monitoringinsects, plant development, soil health, and recommending correctiveactions. Agricultural products that are needed by the growers may beprovided by agricultural producers and distributed by agriculturaldistributors.

It is with respect to these and other considerations that the disclosuremade herein is presented.

BRIEF DESCRIPTION OF THE DRAWINGS

The Detailed Description is set forth with reference to the accompanyingfigures.

FIG. 1 is a system diagram for horticultural operations.

FIG. 2 is a block diagram of an example hardware, software andcommunications environment for horticultural operation.

FIG. 3 is a block diagram of an example hardware, software andcommunications environment for horticultural operation.

FIG. 4 is a block diagram of an example hardware, software andcommunications environment for horticultural operation.

FIG. 5 is a block diagram of an example system for horticulturaloperation.

FIG. 6 is a flow diagram showing aspects of an illustrative routine,according to one embodiment disclosed herein.

FIG. 7 is a flow diagram showing aspects of an illustrative routine,according to one embodiment disclosed herein.

FIG. 8 is a flow diagram showing aspects of an illustrative routine,according to one embodiment disclosed herein.

DETAILED DESCRIPTION

The environments surrounding different horticultural grow operations canvary widely. A horticultural operation is comprised of a set of plantsto be grown (also called a “grow”), a set of processes to plant, grow,maintain, harvest and document the set of plants, and the feedstockincluding but not limited to seed, plant material, fertilizer,pesticides and the like. Horticultural operations may be indoor ingreenhouses, outdoor, may be localized or geographically disparate, andmay be automated.

Information collected from grow operations can be of low-fidelity,difficult to associate with the source of information, as well asassociate with the plant under test, untimely, and incomplete. Withouthigh-fidelity, reliable, timely, and complete information, and withoutcoordinated data collection, analysis, and remediation, the ability togenerate accurate recommendations to farmers/growers for remedial actionmay be difficult or impossible. Additionally, existing flows ofinformation between PCA/agronomists, agricultural producers,agricultural distributors, and farmers/growers may have inefficienciesthat make it difficult for farmers/growers to identify what agriculturalproducts they need and to be GRC compliant and obtain the products atefficient prices. Furthermore, the flows of information may limit theuse of sensors, autonomous devices, and other technological innovationsby the farmers/growers.

With regard to the roles in horticultural operations, PCA/agronomistsare generally licensed, and their recommendations are typically made tosatisfy governance/regulatory/compliance concerns. Accordingly, suchrecommendations may be quite costly. Furthermore, some PCA/agronomistsmay be employed by the suppliers, which may introduce biases in therecommendations. It is desirable to implement a platform that canprovide such recommendations that disintermediate PCA/agronomists orother roles in horticultural operations and enable greater efficienciesand improve recommendations provided to the farmers/growers.

Technologies and techniques provided herein enable the implementation ofa platform for collecting and analyzing high-fidelity data fromgreenhouses or other growing sites. The analysis of the high-fidelitydata may enable the platform to generate timely and accuraterecommendations for designing new feedstock, mixtures/combinations ofexisting feedstock, optimizing workflow of an agricultural operation,and continuously modifying and updating the recommendations based onadditional data as the data becomes available. In some embodiments, theplatform may further be configured to provide product recommendationsand implement a bidding platform through which producers anddistributors can bid for the lowest price to sell to farmers andgrowers. Such a platform may be referred to herein as an agro platform,agricultural platform, agricultural exchange, or agricultural exchangeservice.

The platform may enable the continuous monitoring of a horticulturaloperation to collect information about the operation, identify problemsin the operation, identify solutions to those problems, and generaterecommendations for remediation. Furthermore, the collection ofhigh-fidelity data can enable the inclusion of a large number ofvariables including factors that may vary across location, e.g. people,climate, mechanical problems, and the like, that provide contextualinformation around plant measurements. In some implementations,high-fidelity data can include data where sampling is performed at leastonce before any “event of interest.” In typical operations, a plant maybe imaged once a week. With high-fidelity data, images may be capturedseveral times per day, allowing plant images to be captured between eachand every action performed by an agricultural worker. This can enablegreater fidelity and accuracy as well as generation of results that werenot previously possible.

In one embodiment, individual or groups of plants may be continuouslymonitored with one or more sensors. For example, one sensor can be animage capture device, such as digital video camera, or a still imagedigital camera configured to take still images periodically or on someother schedule. The images may be collected and analyzed usingobject-recognition techniques and computer image analysis techniques togenerate information for accurate automated diagnosis andrecommendations for remediation. In some embodiments, recommendationsmay be determined using a machine learning model.

Advantages of such a platform may include the completeness and speedthat remediation recommendations may be dispatched to growers. Since theindividual plants and their respective environments are being constantlymonitored, the platform may enable real-time or near real-time responseand monitoring. An additional advantage is the comprehensive collectionof data across an entire horticultural operation. The platform maycollect information for every plant in a given operation and itsenvironment. Historical information may be retained, enabling theidentification of trends over time, comparison to similar plants, andidentification of relationships between plants that were not previouslyidentified. A further advantage is that the use of computer analysis mayallow for analysis of specific portions of a plant as well aschronological analysis of images over time, enabling diagnosticpossibilities that may further inform the recommendation and biddingprocess.

The platform may further enable a more efficient supply and demandprocess which may lead to decreased costs for agricultural products,while enabling improved recommendations for agricultural products. Forexample, more customized recommendations can be generated for a grow,lower costs of feedstock can be optimized, and workflow for agro workerscan be optimized, to name a few. The platform may further enable variousthird parties to participate in the traditional producer distributorPCA/agronomist grower model.

In an embodiment, the platform may be implemented as one or morecomputing devices that are configured to:

Communicate with one or more sensors (e.g., Image Capture Device (ICD))that captures data at different grower sites

Receive data from the sensors

Perform analysis of the sensor data

Identify diagnoses based on the analysis

Generate recommendations based on the analysis and diagnoses

Receive proposals for agricultural products

Receive a selection for an agricultural product

Continuously update the analysis using feedback

The platform may be configured to provide diagnosis and recommendationinformation to producers, distributors, PCA/agronomists, growers, orthird parties (e.g., researchers and regulators). Producers may use theinformation to determine which agricultural products to propose togrowers, and distributors can use the information to determine whichagricultural products to deliver. PCA/agronomists can use theinformation to determine whether the diagnosis/recommendations arecorrect or whether modifications are needed. Growers can use theinformation to determine which recommendations to incorporate, whichagricultural product to obtain, recommendations for making a customfeedstock, etc. The recommendations may include, for example, growermethods, feedstock supplies and mixtures, workflow, Lumière parameters,plant species, and the like. For example, the amount of light plusspectrum and temperature of light may be controlled. The platform mayuse the high-fidelity data to continuously fine tune and updaterecommendations for these and other parameters.

The platform can further enable producers and distributors to bid forthe lowest price to sell to growers and enable growers to select aproposed bid. The platform thus provides an independent source ofanalysis and recommendations for growers, while providing an efficientplatform that can provide accurate and timely information and enableefficient transactions between the parties.

The platform can be configured to provide interfaces for exchanginginformation with various other systems and devices at one or more ofproducers, distributors, PCA/agronomists, growers, as well as otherparties. The various parties may interface to the platform using varioustypes of devices including mobile devices to enable continuous andas-needed communications. For example, an online exchange of information“recipes” as well as an online store (similar to an app store) may beimplemented. The platform may further implement interfaces that enableadditional parties to contribute to or receive data from the platform,such as research facilities, universities, regulatory agencies (e.g.,demonstrating compliance with an agricultural compliance plan), and thelike. In some embodiments, an application programming interface (API)may be provided to facilitate the servicing of input and output to theplatform.

It should be appreciated that the above-described subject matter may beimplemented as a computer-controlled apparatus, a computer process, acomputing system, or as an article of manufacture such as acomputer-readable storage medium. While the examples described hereinare illustrated in the context of agricultural processes, it should beunderstood that the described principles can be implemented with othertypes of growing processes pertaining to plants and animals. These andvarious other features will be apparent from a reading of this DetailedDescription and a review of the associated drawings. Furthermore, theclaimed subject matter is not limited to implementations that solve anyor all disadvantages noted in any part of this disclosure.

In the example system illustrated in FIG. 1, a system 100 is illustratedthat implements platform 110. The platform 110 may be configured toreceive input from and provide information to various devices 150 over anetwork 120, as well as computing device 130. A user interface 160 maybe rendered on computing device 130. The user interface 160 may beprovided in conjunction with an application 140 that communicates to theplatform 110 using an API via network 120. In some embodiments, system100 may be configured to provide agricultural information to users. Inone example, platform 110 may be configured to receive input from one ormore grow operations, analyze the data, and provide recommendations tocomputing device 130 and various devices 150.

In an embodiment, an agricultural machine learning or cognitive networkmodel may be implemented with a feedback loop to update therecommendations based on currently available data. At least some of thisdata may be data shared by growers. Growers may be provided an option toopt in/opt out of the system for privacy. In some configurations, theagricultural machine learning model may be configured to utilizesupervised, unsupervised, or reinforcement learning techniques togenerate diagnoses and recommendations. For example, the agriculturalmachine learning model may utilize supervised machine learningtechniques by training on sensor data and user data as described herein.In some embodiments, the machine learning model may also, oralternatively, utilize unsupervised machine learning techniques togenerate diagnoses and recommendations including, but not limited to, aclustering-based model, a forecasting-based model, a smoothing-basedmodel, or another type of unsupervised machine learning model. In someembodiments, the machine learning model may also, or alternately,utilize reinforcement learning techniques to generate diagnoses andrecommendations. For example, the model may be trained using the inputdata and, based on grower feedback, the model may be rewarded based onits output.

In some embodiments, the agricultural data may be analyzed to identifytrends and patterns related to diagnoses and recommendations. Forexample, diagnoses may be for plant health issues as well as fordetrimental workflows. The agricultural data may be analyzed todetermine which recommendations may influence grower behavior andinteraction, and in some cases, which product diagnoses andrecommendations may be related to an increased likelihood of growerbehavior such as increasing the likelihood of purchasing a recommendedproduct or modifying a workflow. In one embodiment, the agriculturalmachine learning model may incorporate a classification function thatmay be configured to determine which diagnoses and recommendations arerelevant for a particular objective, such as optimizing for yield,optimizing for economics, optimizing for a particular plant result(e.g., spotted pink roses), etc. The classification function may, forexample, continuously learn which diagnoses and recommendations arerelevant to various potential outcomes. In some embodiments, supervisedlearning may be incorporated where the machine learning model mayclassify observations made from various sensor data and user data, andpotentially from pre-existing information frameworks. The machinelearning model may assign metadata to the observations. The metadata maybe updated by the machine learning model to update relevance to theobjectives of interest, as new observations are made, and assign tags tothe new observations. The machine learning model may learn whichobservations are alike and assign metadata to identify theseobservations. The machine learning model may classify futureobservations into categories.

In some embodiments, an algorithm, such as a feature subset selectionalgorithm or an induction algorithm, may be implemented to definegroupings or categories. Probabilistic approaches may also beincorporated. One or more estimation methods may be incorporated, suchas a parametric classification technique. In various embodiments, themachine learning model may employ a combination of probabilistic andheuristic methods to guide and narrow the data that are analyzed.

In order to provide relevant results that are more likely to indicateoutcomes for a particular observed pattern of data, the most relevantpatterns may be identified and weighted. In some embodiments a heuristicmodel can be used to determine diagnoses and recommendations thatprovide an acceptable confidence level in the results. For example,experience-based techniques, such as expert modeling can be used to aidin the initial selection of parameters. The heuristic model canprobabilistically indicate parameters of likely impact through, forexample, tagging various metadata related to a particular pattern.Feedback from an initial round of analysis can be used to further refinethe initial selection, thus implementing a closed loop system thatgenerates likely candidates for diagnoses and recommendations insituations where programmatic approaches may be impractical orinfeasible. As an example, Markov modeling or variations thereof (e.g.,hidden Markov model and hierarchical hidden Markov model) can be used insome embodiments to identify candidate diagnoses and recommendationsthat may otherwise be missed using traditional methods.

In an embodiment, the agricultural machine learning model may beconfigured to learn the effect of a recommendation on a species under agiven set of conditions that exist for a grow operation using aclassical or deep reinforcement learning method, supervised learningmethod, or other machine learning method. A recommendation can includeany permutation of a given set of products, treatments, or recipes.Using the deep reinforcement learning path as an example, a “policy”network can be trained to generate potential permutations of products,treatments, or recipies, while a “value” network would be trained toidentify the policies that give the grower the best outcome. Used inthis way, the machine learning model can, for example, makerecommendations to the grower in order to optimize some part of theirgrow operation, or, in the case of a single product permutation,identify products that have different effects than advertised.Theeffects of recommendations can be monitored through manual measurementsof the growth process, for example through yield of the crop, or in anautomated way using measurement devices as described herein. As thesystem accumulates more automated and manual grower measurements, pairedwith system recommendations and advertised product effects, theagricultural machine learning model can be continuously improved.

In an embodiment, a grading or quality assessment of selected items inthe agricultural marketplace can be provided to a user for a given issueidentified by the agricultural machine learning model. For example, ifGrower A has fusarium, a list of fusarium treatment products could berecommended, each with a visible quality metric to help the grower makeinformed purchases. The quality metric may be derived from theagricultural machine learning model's identification of an item on themarketplace to solve a target problem or set of problems based on theprior knowledge of all instances of the use of that product for thistype of problem. It should be noted that the quality metric may also bemulti-dimensional and convey, for example, the potential for pestinfestation, the effect on yield, and the effects on drought-tolerance.

In order to encourage knowledge sharing, users can offer more detailedinformation for their recipes (e.g., their watering schedule, lightingschedule, temperature zones, fertilizer, etc.), contributing to theglobal knowledge pool of the system. The users may receive in returnrecipes that have been built using this global knowledge, thus providingmore useful information than any one grower's recipe alone. In someinstances, growers may not always use the quality metrics generated bythe agricultural machine learning model to select the products theywhich to purchase. Some growers may opt to use their own expertise tomake these decisions, which in turn can provide novel combinations ofrecipes/species/treatments/products/locations that can be input to theagricultural machine learning model and thus further improving theself-supervised learning process.

FIG. 2A depicts an example of an example horticultural operation 200.Horticultural operation 200 may cover one or more locations, such asgreenhouses 202. Greenhouses 202 may also encompass any location orfacility where plants are grown such as an open field, a hydroponicoperation, and/or so forth.

Greenhouse 202 may have one or more grow operations 204 each with one ormore plants 206. A grow operation 204 may include multiple plants indifferent locations/greenhouses 202. A grow operation 204 may be alogical grouping of plants 206 that are similarly situated such that thecultivation of each plant in the group is substantially similar.Greenhouse 204 may have multiple grow operations, for example, differentgrow operations for different type of plants. However, each growoperation may also have more than one type of plant under care. Growoperation may also be referred to as horticultural operation.

Information for the plants may be captured with a sensor device 207,which in one example may be an image capture device 208. Each plant 206may be monitored by at least one image capture device 208. In someembodiments, each individual plant may have a single dedicated imagecapture device 208. The image capture device may be a digital videocamera or may be a still image camera configured to capture imagesperiodically and/or on demand.

Generally, an image capture device 208 may take visible light spectrapictures but may also extend to non-visible spectra such as infrared andultraviolet. The image capture device 208 may have an on-boardapplication programming interface (API) enabling programmatic control.Alternatively, the image capture device 208 may be networked to enableremote control.

Referring to FIG. 3, the image capture device 308 may be part of alarger suite of sensors networked to a data capture function whichuploads plant, telemetry, media, and other data such as plant orenvironmental health status to a server 328 at platform 330 incommunication with network 324. For example, sensors may collecttelemetry on a per plant or substantially per plant basis. The samplingrate may be high-fidelity and thus an image associated with any event ofinterest may be available. Without limitation, sensors may include lightmeters, water meters, potential of hydrogen (pH) acid/alkali meters, andthe like. It is noted that any sensor that may be connected to astandard computer input/output interface may be added.

Telemetry may include various types of data, including directly measureddata and derived data. For example, a light meter may measure lightintensity for that moment of time, and an extrapolation calculation mayestimate the daily light integral, which is the total light applied to aplant over a given time period. Telemetry from different sensors mayalso be combined. For example, a light meter may provide a measurementof the amount of light over time and an oxygen sensor may measure anamount of O₂ generated by a plant over time. From these twomeasurements, the photosynthetic efficiency measurements, such as therelative photosynthesis index may be calculated. Telemetry from sensorsmay be combined with outside information. For example, a sensorproviding telemetry for the amount of vapor in the air may be combinedwith the water saturation point, to calculate the vapor pressuredeficit. The vapor pressure deficit is the difference between the amountof water in the air and the amount of water the air can hold ifsaturated.

The image capture device 306 may be configured to upload capturedimages, annotations, and/or other data to the platform 330 (e.g.,platform server 328). The platform server 328 can comprise any computingdevice with a processor, a memory, and a network interface that mayparticipate in a network. The network 324 may be, without limitation, alocal area network (“LAN”), a virtual private network (“VPN”), acellular network, a cloud, the Internet, and/or so forth.

The platform server 328 may be configured to perform image analysis ofimages of interest in order to recognize images, annotations, and/orother data, automatically detect issues in plants, and detect otherissues related to grow operations. In various embodiments, uponreceiving an image, the platform server 328 can identify a target, anartifact of the target, and/or an identified issue record within thereceived image and classify the target, the artifact of the target,and/or the identified issue record to rank and sort the image. Based atleast partially on the received image, the platform server 328 canassociate the image with an issue record in order to retrievecorresponding recommended courses of action to remediate the detectedissue and other relevant data. In this way, for instance, the platformserver 328 can assess the health of a plant in a grow operation at anarbitrary time and provide care recommendations to an operator on site.Because historical data has been collected and is available, there is noneed to wait for a recommendation. Additionally, the recommendations canbe optimized over time. Plants may be marked for continued monitoring tochange the recommendations over time. For example, a tobacco mosaicdiseased plant may need X1 medicine at time T1, but X2 medicine at timeT2. Another example is that a flower may have density D1 of aphids at T1but D2 at T2. Recommendations can thus be made in a dynamic and timedependent manner.

In various embodiments, the platform server 328 can also associate theimage with an issue record in order to retrieve correspondingrecommended courses of action previously taken or attempted to remediateother previous issues or the same issue detected. In this way, certaincourses of actions can be ruled out if they were previously unsuccessfulin remedying the issues or led to subsequent issues. Alternatively,certain courses of actions can be reattempted if they were previouslysuccessful in remedying the issues or similar issues.

In various embodiments, the platform server 328 may process image datareceived by one or more sensors. In doing so, the platform server 328may identify particular plants 308, identify issues associated withthose plants 308, and determine corresponding courses of action.

The platform server 328 may be configured to manage the coordination ofinformation to different users/operators. The platform server 328 makesavailable the images and results of the image processing analysis to amachine learning engine, to administrative personnel responsible foroverseeing the horticultural operation 302, and/or an operator 332,responsible for at least some of the grow operations. Administrativepersonnel can manually review the state of plants within thehorticultural operation 302, identify issues, and direct remedialcourses of action to address those identified issues, depending uponembodiments.

In various embodiments, the platform server 328 may create an issuerecord by performing image processing on an image. Thus, the platformserver 328 may be configured to identify issues in plants and otherissues related to grow operations based at least partially on receivedimages from an image capture device 308. In addition to identifyingissues, the platform server 328 may also store a table of remediationcourses of action associated with the issues. In this way, where anissue is associated with an image, a record may be generated and used toquery the platform server 328 for at least one remediation course ofaction. In some cases, the remediation course of action can be apreviously attempted remediation course of action or a new remediationcourse of action. In some embodiments, the platform server 328 maydetect when recommendations are different from prior recommendations. Itcan be determined that in such cases, experimental data may be used tochange recommendations for other growers.

In response to an association of an issue with an image and theassociation of the issue with at least one remediation course of action,the platform server 328 may send a message or a notification comprisinga description of the issue (e.g., in a plant) and other information(e.g., related telemetry and/or media, previous attempts/courses ofactions to remedy other or same issues, etc.) to a user device 330.

The platform server 328 can also make images and/or annotationsavailable to a platform interface program 322 on demand so users canbrowse images and/or annotations and create an issue record using, forexample, user device 330. In various embodiments, the platform server328 may interact with producers 334, distributors 335, andPCA/agronomists 336.

The user device 330 may generally be a mobile device or another type ofhandheld network-enabled electronic device such as a laptop, a tabletcomputer, and/or a cell phone. As with any computing device, the userdevice 330 comprises a processor, a memory, and a network interface withanalogous characteristics to the servers as described above. The userdevice 330 may also include an input/output interface such as a touchscreen display. The dispatching device comprises 330 software componentsto receive, analyze, and report status updates or other information,communicate with administrative personnel or devices at producers 334,distributors 335, and PCA/agronomists 336, and analyze and diagnosepotential issues in plants and horticultural operations.

The platform server 328 may have access to a data store 330 (e.g., afile server, a network-aware storage, a database, etc.), eitherintegrated or accessible via network such that images and/or annotationscan be stored in a database in an image table, issue records can bestored in an issue table, and remediation courses of action can bestored in a solutions table. In a relational database embodiment, across-reference table relating images to issues would then storeassociations of images to issues, and another cross-reference tablerelating issues to one or more courses of action would storeassociations of issues to remediation courses of action. Alternatively,images and/or annotations may store a pointer to an issue record and oneor more courses of action as part of the image.

Referring again to FIG. 2, in some embodiments the image capture device208 may work in concert with a lumière feedback device 210. The lumièrefeedback device 210 provides light on a plant 206 and may be configuredto change spectrum and intensity of the light on the plant 206 based onfeedback from sensors. One of the sensors may be the image capturedevice 208 and the analysis of the images captured by the image capturedevice 208. In some embodiments, the lumière feedback device 210 mayincorporate the image capture device 208. Furthermore, the lumièrefeedback device 210 may be networked. Accordingly, the lumière feedbackdevice 210 may use internal logic to capture images with the imagecapture device 208 and adjust light spectrum and/or intensity.Alternatively, the lumière feedback device 210 may share images andother information to a central location for further analysis. Uponcompletion of this analysis, the lumière feedback device 210 may beconfigured to adjust light spectrum and/or intensity according to aremediation course of action, comprising one or more tasks to address anidentified problem, thereby completing a feedback loop.

Where the lumière feedback device 210 is to share images and otherinformation to a central location containing image analysis services212, the lumière feedback device 210 may either send images directly tothose image analysis services 212 or may queue those images in anintermediate server 214 which in turn may subsequently forward thoseimages to the image analysis services 212. The intermediate servers maydirectly send images to those services 212 if the services 212 are onthe same network. Alternatively, the intermediate servers 214, may routeimages to the image analysis services 212 via the internet and/or cloudservices 216. In other embodiments, the image analysis services may behosted in a virtual machine on the cloud. In some cases, theintermediate server 214 may be on premises, or alternatively, may behosted off premises.

The image analysis services 212 may comprise a plurality of individualservices to perform an analysis workflow on images. Those services mayinclude one or more image reception software components 218 to receiveimages sent by image capture devices 208, lumière feedback devices 210,intermediate servers 214, or other sources of a grow operation 204.

The one or more image reception software components 218 will then placeone or more images in a memory buffer 220 where additional imageprocessing services will be applied. Specifically, one or more imagepreprocessing software components 222, one or more classificationsoftware components 224, one or more analysis software components 226may be applied to an image in a buffer 220. Once the applications arecompleted, an image in a buffer 220 may be persisted and aggregated in adata store 228.

The result of the image analysis services 222 is not only to analyzereceived images, but also to identify problems and to identify potentialsolutions. Specifically, once received images are analyzed, a course ofaction for remediation may be identified. Once the image analysisservices 222 identifies at least one course of action for remediation,it may interact directly with a grow operation via the image capturedevice 208, the lumière feedback devices 210, intermediate servers 214,or other interfaces to a grow operation 204.

Alternatively, one or more courses of action for remediation may betransmitted to a master grower 230 responsible for at least one growoperation and/or a line worker 232 who is to perform the actual taskscomprising a course of action for remediation. In one embodiment, all ora portion of the course of action for remediation may be displayed in ahorticultural management device 234 for view and interaction by themaster grower 230 and/or line worker 232. The horticultural managementdevice 234 may be any networked computer, including mobile tablets overWi-Fi and/or mobile tablets over a cellular network and/or laptop(s).The horticultural management device 234 may connect to the cloud 216,directly to the image analysis services 222, or directly to the growoperation 204, via intermediate servers 214, lumière feedback devices210, image capture devices 208, or other interfaces to the growoperation 204.

FIG. 4 illustrates an embodiment of a hardware, software andcommunications environment 400. In an embodiment, images are capturedvia an image capture device 108. Generally, an image capture device 108may take visible light spectra pictures but may also extend tonon-visible spectra such as infrared and ultraviolet. The image capturedevice 108 may have an on-board application programming interface (API)enabling programmatic control. Alternatively the image capture device108 may be networked thereby enabling remote control.

Control functions for image capture may be in a separate image capturefunction 402. The image capture function 402 may incorporate the imagecapture device 108 and may be part of a larger integrated device, suchas a lumière feedback device 310. Indeed, the image capture function 402may be part of a lumière feedback device 310.

The image capture control function 402 may generally be hosted on acomputing device. Exemplary computing devices include without limitationpersonal computers, laptops, embedded devices, tablet computers, smartphones, and virtual machines. In many cases, computing devices arenetworked.

The computing device for the image capture control function 402 may havea processor 404, a memory 406. The processor may be a central processingunit, a repurposed graphical processing unit, and/or a dedicatedcontroller such as a microcontroller. The computing device for the imagecapture control function 402 may further include an input/output (I/O)interface 408, and/or a network interface 410. The I/O interface 408 maybe any controller card, such as a universal asynchronousreceiver/transmitter (UART) used in conjunction with a standard I/Ointerface protocol such as RS-432 and/or Universal Serial Bus (USB). Thenetwork interface 410, may potentially work in concert with the I/Ointerface 408 and may be a network interface card supporting Ethernetand/or Wi-Fi and/or any number of other physical and/or datalinkprotocols.

Memory 406 is any computer-readable media which may store severalsoftware components including an operating system 410 and softwarecomponents such as an image flow controller 414 and/or otherapplications 416. In general, a software component is a set of computerexecutable instructions stored together as a discrete whole. Examples ofsoftware components include binary executables such as static libraries,dynamically linked libraries, and executable programs. Other examples ofsoftware components include interpreted executables that are executed ona run time such as servlets, applets, p-Code binaries, and Javabinaries. Software components may run in kernel mode and/or user mode.

Computer-readable media includes, at least, two types ofcomputer-readable media, namely computer storage media andcommunications media. Computer storage media includes volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules, or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other non-transmission medium that can be used to storeinformation for access by a computing device. In contrast, communicationmedia may embody computer readable instructions, data structures,program modules, or other data in a modulated data signal, such as acarrier wave, or other transmission mechanism. As defined herein,computer storage media does not include communication media.

In an embodiment, image flow controller 414 is a software componentresponsible for managing the capture of images, receiving images fromthe image capture device 108 (if not integrated with the image capturefunction 402), the local management of received images, and potentiallythe transmission of received images off the image capture function 402over a network. The image flow controller 414 may store a configurationsetting of how many images an image capture device 108 is to capture,the resolution the image is to be captured, the format the image is tobe stored, and any other processing to be performed on the image. Theimage flow controller 414 may store a captured and/or received imageinto a buffer in the memory 406 and name the filename of the receivedimage. Other applications 416 may be utilities to perform imageprocessing, such as compression and/or encryption.

The image flow controller 414 may also manage the transmission ofreceived images. Specifically, it may transmit an image to a knownnetwork location via the network interface 410. The known networklocations may include an intermediate server 314, an internet and/orcloud location 316 or an image processing server 418.

Upon transmission, the image flow controller 414 may enlist innotifications to determine that the transmission was successful. Theimage flow controller 414 may also transmit notifications to otherdevice subscribing to its notifications indicating status of atransmission.

The image capture function 402 may communicate to an intermediate server314. The intermediate server 314 is any computing device that mayparticipate in a network. The network may be, without limitation, alocal area network (“LAN”), a virtual private network (“VPN”), acellular network, or the Internet. The intermediate server 414 issimilar to the host computer for the image capture function.Specifically, it will include a processor, a memory, an input/outputinterface and/or a network interface. In the memory will be an operatingsystem and software components to route images. The role of theintermediate server 414 is to forward images received from the imagecapture functions 402 and to forward directly, if on the same network,to an image processing server 418, or via the internet and/or cloud 316.In some embodiments, the intermediate server may act as intermediatestorage for images.

A service on the cloud 316 may provide the services of an intermediateserver 414, or alternatively may host the image processing server 418. Aserver, either intermediate 414, or for image processing 418, may eitherbe a physical dedicated server or may be a virtual machine. In thelatter case, the cloud 418 may represent a plurality of disaggregatedservers which provide virtual application server 440 functionality andvirtual storage/database 422 functionality. The disaggregated serversare physical computer servers, which may have a processor, a memory, anI/O interface and/or a network interface. The features and variations ofthe processor, the memory, the I/O interface and the network interfaceare substantially similar to those described for the host of the imagecapture function 402, and the intermediate server 414. Differences maybe where the disaggregated servers are optimized for throughput and/orfor disaggregation.

Cloud 418 services 440 and 422 may be made accessible via an integratedcloud infrastructure 444. Cloud infrastructure 444 not only providesaccess to cloud services 440 and 422 but also to billing services andother monetization services. Cloud infrastructure 444 may provideadditional service abstractions such as Platform as a Service (“PAAS”),Infrastructure as a Service (“IAAS”), and Software as a Service(“SAAS”).

The image processing server 418, is generally a computer server or on avirtual machine. Where the image processing server 418 is a physicalcomputer server, it may have a processor 426, a memory 428, an I/Ointerface 430 and/or a network interface 432. The features andvariations of the processor 426, the memory 428, the I/O interface 430and the network interface 432 are substantially similar to thosedescribed for the host of the image capture function 402, and theintermediate server 314.

The memory 428 of the image processing server 418, will store anoperating system 434 and a set of software components to perform imageanalysis services 312. Those software components may include, an imageretriever software component 436, an image buffer in memory 438, animage preprocessor software component 440 which may further include oneor more image preprocessing algorithms 442, a classifier softwarecomponent 444, an identifier software component 446 which may furtherinclude one or more identifier algorithms 448, and an analyzer softwarecomponent 450.

The image retriever software component 436 manages the receiving ofimages from image capture functions 402. The throughput of images andsupplementary data may differ. Accordingly, the image retriever softwarecomponent 436 may manage the timing, speed, and the party controllingthe data transfer. For example, it may act as a simple store, whichreceives and stores images upon receipt as pushed by an image capturefunction 402. Alternatively, it may affirmatively pull images for imagecapture functions.

One example of a pull scenario is where an image processing server 418is first joining the network. When this happens, one or more imagecapture functions 402 could potentially overload the image processingserver 418 by sending a large number of images. To prevent overload, theimage retriever software component 436 will negotiate a controlledtransfer with the one or more image capture functions 402. An example ofnegotiated controlled transfer is described with respect to FIG. 4.

When an image retriever software component receives an image 436, it maystore the received image in an image buffer 438. An image buffer 438 isdedicated memory, generally part of the memory 428, where a retrievedimage may reside to be processed. Common image buffers are contiguousdedicated RAM, where the data comprising an image may be accesseddirectly rather than via a series of central processing unit commands.Generally such a configuration is via a Graphical Processing Unit.

Once an image is in the buffer 438, the image may be subjected to one ormore image processing and analysis operations. An image preprocessorsoftware component 440 performs any transformations to an image enableanalysis to increase the likelihood of successful analysis. Exampleoperations to enable analysis are to decompress and/or decrypt incomingimages via the respective decompression and/or decryption algorithms442. Example operations to increase the likelihood of successfulanalysis is to apply one or more transformations and/or content analysisalgorithms 442 are Gaussian blur and Red-Green-Blue (RGB) contentanalysis. The aforementioned algorithms 442 as well as other algorithms442 applied by the image preprocessor software component 440 aredescribed in further detail with respect to FIG. 4.

Generally, analysis is performed later in the image workflow of theimage processing server 418. Where possible, algorithms 442 attempt totake partial images, corrupt images, or otherwise substandard images andapply corrections sufficient to support analysis. However, the imagepreprocessing software component 440 may also contain logic to removeimages with insufficient information or quality from the workflow. Inthis way, data collected during subsequent analysis will not containdata from corrupt or misleading images. This cleaning logic may be partof the image processing software component 440 or alternatively may bein a separate image cleaning software component.

Once preprocessing is complete, the classifier software component 444 isconfigured to identify which portions of an image represent the plant tobe analyzed as opposed to portions of the image representing items otherthan the plant to be analyzed. The classifier software component 444identifies discrete objects within the received image and classifiesthose objects by a size and image values, either separately or incombination. Example image values include inertia ratio, contour area,and Red-Green-Blue components. Based on those values, the objects areranked and sorted. Items above a predetermined threshold, or the highestN objects, are selected as portions of the received image representingthe plant. The classifier software component 444 is described in furtherdetail with respect to FIG. 4.

After classification, an identifier software component 446 is configuredto identify the plant in the received image and to identify artifacts inthe plant. This involves comparing the image data of the plant in thereceived image to that of other images. In order to perform thosecomparisons, the identifier software component 446 may create a plantstate vector comprised of values and value sets generated by one or morealgorithms 448 of the identifier software component 446. Such aconstructed vector corresponds to the state of a plant in an image andis compared against other plant state vectors to perform generalcomparisons as well as sequential analysis.

The identifier software component 446 contains several identificationalgorithms 448. Some algorithms 448 work directly on a single image.Other algorithms 448 may process a series of images classified togetherinto a category, collect information in common, and apply to subsequentimages. Example categories may be images of the same plant over time,images of the same genus and species of plant, and images of plantsgiven the same care.

One example of the latter case is where the identifier softwarecomponent 446 collects color histogram data over a plurality of imagesof the same category and generates an average histogram comprised of theaverages or weighted averages of each distribution variable comprisingthe histogram. Accordingly, when an image is received belonging to thesame category, the identifier software component 446 may use the averagehistogram to identify the plant and artifacts in the plant. The averagehistogram is then recalculated using the histogram of the incomingimage. In this way, the average histogram becomes an adaptive histogramwith improving performance. In some embodiments, the logic to performanalysis using data from a plurality of images, or performingcomputationally intense logic, may be separated from the identifiersoftware component 446 into another software component such as an edgecleaner software component.

In some embodiments, deep-learning/machine learning can be used toencode qualities of interest of a plant into the plant state vector.

As previously mentioned, transforming a raw received image into a statethat can be analyzed is only part of the function of the imageprocessing server 418. Another function is the analysis of thetransformed image. The analyzer software component 450 takes thetransformed image, and potentially any generated additional information,such as a plant state vector, and maps portions of the image to indiciacorresponding to a feature of a plant. An indicium (of the indicia) iscalled an artifact. Because the classifier software component 444identified objects comprising portions of a plant, those portions may besubjected to analysis of visual information. Because the identifiersoftware component 446 may have generated branch information about plantbranches, leaf structure, and root structure, branch analysis mayidentify not only artifacts but artifacts indicating issues in theplant.

If at least one artifact corresponds to an issue with a plant, theanalyzer software component 450 may also retrieve correspondingrecommended courses of action to remediate the issue. Such informationmay be subsequently sent to the grow operation 104, intermediate server314, lumière feedback device 310, image capture device 108, and or otherentry points into the grow operation 104.

The image processing server 418 may have access to a data store 452,either integrated (not shown) or accessible via network. The imageprocessing server may store raw images, transformed images, generatedplant state vectors, and other related information for archival and/orreporting after processing is complete. The data store 452 may beconfigured as a relational database, an object-oriented database, aNoSQL database, and/or a columnar database, or any configuration tosupport scalable persistence.

Reporting may be performed by a querying software component (not shown).Because each image is associated with a plant, date/time stamp, plantstate vector, and potentially identified issues, images may be queriedby any or all of these data values.

The disclosed embodiments provide infrastructure capable of collectingimage and other information on a per plant basis, applying sophisticatedimage analysis, applying sophisticated horticultural analysis todiagnose problems and recommend a remedial course of action, all whiledistributing the relevant information to workers and or devices in thegrow operation.

FIG. 5 illustrates a block diagram of a bidding platform 502 that mayimplement a bidding function based on bidding input received fromvarious parties. In doing so, the bidding platform 502 may facilitatethe receipt and processing of bids for agricultural products and/orservices based on input from suppliers such as distributors andproducers. The bidding platform 502 may correspond to at least a part ofplatform 310. In the illustrated example, the bidding platform 502 mayinclude one or more processor(s) 504 operably connected to memory 506.

In the illustrated example, the memory 506 may include an operatingsystem 508, a bid collection module 510, a bid processing module 512, aselection module 514, a data-store 516, and a user interface 518. Theoperating system 508 may be any operating system capable of managingcomputer hardware and software resources.

In the illustrated example, the bid collection module 510 may aggregatethe bids received from various parties to generate input for theprocessing and selection process. The bid data may include bids forproducts and services that were recommended by platform 310.

In the illustrated example, the bid processing module 512 may generate aproposal using, as input, the data from the bid collection module 510.In the illustrated example, the data-store 516 may store accumulated bidand selection data for transactions facilitated by the platform.

In the illustrated example, the user interface 518 may be configured todisplay notifications that alert suppliers and consumer plant growers ofthe progress of a bid.

In various examples, the user interface 518 may provide a means for aplant grower to receive bids. In some examples, the user interface 518may also provide a means for a plant grower to request and select bidsfor plant growth scripts from merchants via the script offering system.The user interface 518 may also allow plant growers to specifyunderlying conditions that solicited bids must meet. In a non-limitingexample, the underlying conditions may include a minimum satisfactionrating of a merchant, or a request for one or more products pertainingto particular plant species.

The bid collector module 510, bid processing module 512, bid selectionmodule 514, or other components of the bidding platform 302 may storebackground information (name, location, account number, login name,passwords, tax identification number, among other information) forparticipants of the bidding platform. The bidding platform 502 may alsocontain hyperlinks to third-party payers that allows growers to fundpayment for a selected bid. The bidding platform 502 may contain variousother modules not shown, such as, a service provider module that allowsthe bidding platform 502 to generate producer and distributor lists andother information.

When a grower receives a recommendation for a product or service, thebidding platform 502 may initiate the bidding process, set the processparameters, such as a suggested price and duration. After the biddinghas completed, the bidding platform 502 may responds to a query as towhether the grower has selected a bid. If the grower has selected a bid,then the grower may be provided access to payment and delivery options.

In the illustrated example, the bidding platform 502 may includeinput/output interface(s) 520 and network interface(s) 522.

FIG. 6 is a diagram illustrating aspects of a routine 600 forimplementing some of the techniques disclosed herein. It should beunderstood by those of ordinary skill in the art that the operations ofthe methods disclosed herein are not necessarily presented in anyparticular order and that performance of some or all of the operationsin an alternative order(s) is possible and is contemplated. Theoperations have been presented in the demonstrated order for ease ofdescription and illustration. Operations may be added, omitted,performed together, and/or performed simultaneously, without departingfrom the scope of the appended claims.

It should also be understood that the illustrated methods can end at anytime and need not be performed in their entireties. Some or alloperations of the methods, and/or substantially equivalent operations,can be performed by execution of computer-readable instructions includedon a computer-storage media, as defined herein. The term“computer-readable instructions,” and variants thereof, as used in thedescription and claims, is used expansively herein to include routines,applications, application modules, program modules, programs,components, data structures, algorithms, and the like. Computer-readableinstructions can be implemented on various system configurations,including single-processor or multiprocessor systems, minicomputers,mainframe computers, personal computers, hand-held computing devices,microprocessor-based, programmable consumer electronics, combinationsthereof, and the like. Although the example routine described below isoperating on a computing device, it can be appreciated that this routinecan be performed on any computing system which may include a number ofcomputers working in concert to perform the operations disclosed herein.

Thus, it should be appreciated that the logical operations describedherein are implemented (1) as a sequence of computer implemented acts orprogram modules running on a computing system such as those describedherein and/or (2) as interconnected machine logic circuits or circuitmodules within the computing system. The implementation is a matter ofchoice dependent on the performance and other requirements of thecomputing system. Accordingly, the logical operations may be implementedin software, in firmware, in special purpose digital logic, and anycombination thereof.

The routine 600 begins at operation 602, which illustrates receivingsensor data from one or more sensors configured to capture data forplants within a plant growth operation.

The routine 600 then proceeds to operation 604, which illustratesaccessing accumulated data associated with other plants in other plantgrowth operations.

Operation 606 illustrates analyzing the sensor data and accumulated datato determine one or more conditions of the plants within the plantgrowth operation.

Operation 608 illustrates, based on the analysis, determining one ormore plant grower actions to improve plant growth.

Operation 610 illustrates transmitting data to a controller deviceassociated with the plant growth operation, the data includinginstructions associated with the one or more plant grower actions.

Operation 612 illustrates determining one or more agricultural productsor services associated with the plant grower actions.

Operation 614 illustrates inputting the determined products or servicesto an agricultural exchange service configured to process electroniccommerce information from servicers of the products or services.

Operation 616 illustrates receiving, via a user interface, one or morebids from the servicers of the products or services, wherein the bidscomprise proposals for providing the products or services.

Operation 618 illustrates facilitating selection and fulfillment of oneof the bids to a recipient of the data.

In an embodiment, a closed loop function configured to generate likelycandidates for diagnoses and recommendation is executed.

In an embodiment, measured effects of products or services on plants aredetermined.

In an embodiment, a grading or quality assessment of the agriculturalproducts or services is provide for an issue identified by anagricultural machine learning model.

In an embodiment, the sensor data includes image data that is capturedcontinuously, based on a time lapse sequence, or based on a triggeringevent.

In an embodiment, the triggering event may include a change in arelative position of an individual plant with its surrounds, based on ananalysis of temporally sequential image data, or an indication that anenvironmental data-point has fallen below a predetermined threshold.

In an embodiment, the one or more sensors include environmental sensorsor image capturing devices, wherein the environmental sensors includingat least one of range-finding sensors, light intensity sensors, lightspectrum sensors, non-contact infra-red temperature sensors, thermalsensors, photoelectric sensors that detect changes in color, carbondioxide uptake sensors, water, pH testing, and oxygen productionsensors.

In an embodiment, the image capturing devices are capable of capturinghyperspectral images, 3D measurements, RGB, monochrome, or thermalimages.

In an embodiment, the servicers of the products or services include oneor more of agricultural producers, agricultural distributors, orPCA/agronomists.

In an embodiment, the agricultural exchange service is furtherconfigured to transmit information pertaining to the one or moreagricultural products or services associated with the plant groweractions to subscribers of the agricultural exchange service and to aninterface for users of the agricultural exchange service.

In an embodiment, the agricultural exchange service is furtherconfigured to transmit information pertaining to the one or moreagricultural products or services associated with the plant groweractions to an interface for users of the agricultural exchange service.

In an embodiment, the agricultural exchange service is furtherconfigured to transmit information pertaining to the one or moreagricultural products or services associated with the plant groweractions to subscribers of the agricultural exchange service and to aninterface for users of the agricultural exchange service

In an embodiment, data indicative of a selection of the one or more bidsis received via the user interface.

In an embodiment, the agricultural exchange service is furtherconfigured to receive, via the interface, additional informationpertaining to the plant growth operation.

In an embodiment, the one or more plant grower actions are furtherdetermined based on the additional information.

In an embodiment, the agricultural exchange service is furtherconfigured to receive, via the interface, additional informationpertaining to the plant growth operation; and the one or more plantgrower actions are further determined based on the additionalinformation.

In an embodiment, a plurality of configurations are presented to theuser with the feature data including configurations with selling dataand discounting data.

In an embodiment, the determine one or more plant grower actions isperformed by a machine learning component

FIG. 7 is a diagram illustrating aspects of a routine 700 forimplementing some of the techniques disclosed herein.

The routine 700 begins at operation 702, which illustrates receivingsensor data from one or more sensors configured to monitor plants withina planning growth operation.

The routine 700 then proceeds to operation 704, which illustratesaccessing accumulated data associated with other plants in other plantgrowth operations.

Operation 707 illustrates analyzing the sensor data and accumulated datato determine one or more conditions of the plants within the plantgrowth operation.

Operation 708 illustrates based on the analysis, determining one or moreplant grower actions to improve plant growth.

Operation 710 illustrates transmitting data to a controller deviceassociated with the plant growth operation. In an embodiment, the dataincludes instructions associated with the one or more plant groweractions. In an embodiment, the data is operable to automatically controlthe controller device to execute the instructions.

Operation 712 illustrates receiving additional sensor data from the oneor more sensors.

Operation 714 illustrates analyzing the additional sensor data todetermine one or more conditions of the plants within the plant growthoperation.

Operation 716 illustrates based on the analysis of the additional sensordata, updating the one or more plant grower actions.

In an embodiment, the one or more plant grower actions include at leastone of changing a light intensity or a light spectrum of existinglighting within the plant growth operation, changing an amount of wateror a frequency of a watering operation with the plant growth operation,changing an amount of nutrients or fertilizer used with the plant growthoperation, or changing a ratio of nutrients to fertilizer that is usedwithin the plant growth operation.

In an embodiment, the agricultural exchange service is furtherconfigured to transmit information pertaining to the one or moreagricultural products or services associated with the plant groweractions to an interface for users of the agricultural exchange service.

In an embodiment, a progress metric of the plant growth operation isdetermined.

In an embodiment, the progress metric is indicative of progress of theplant growth operation relative to predicted milestones.

In an embodiment, it is determined that the controller device isconfigured to automate at least one plant grower action of the one ormore plant grower actions. In an embodiment, the data transmitted to thecontroller devices includes computational instructions that cause thecontroller device to automate the at least one plant grower actionwithin the plant growth operation.

In an embodiment, user data is transmitted to a user device associatedwith a plant grower of the plant growth operation, the user dataindicative of the one or more plant grower actions.

In an embodiment, the analyzing of the additional sensor data comprisesdetermining that at least one plant is experiencing a less than optimalplant growth, based at least in part on the progress metric.

In an embodiment, the updating of the one or more plant grower actionscomprises generating one or more actions to optimize plant growth of theat least one plant.

FIG. 8 is a diagram illustrating aspects of a routine 800 forimplementing some of the techniques disclosed herein.

The routine 800 begins at operation 802, which illustrates receivingsensor data from one or more sensors configured to capture data forplants within a plant growth operation.

The routine 800 then proceeds to operation 804, which illustratesanalyzing the sensor data to determine one or more conditions of theplants within the plant growth operation.

Operation 808 illustrates, based on the analysis, determining one ormore plant grower actions to improve plant growth.

Operation 808 illustrates determining one or more agricultural productsor services associated with the plant grower actions.

Operation 810 illustrates inputting the determined products or servicesto an agricultural exchange service configured to process electroniccommerce information from servicers of the products or services.

Operation 812 illustrates receiving, via a user interface, one or morebids from the servicers of the products or services.

Operation 814 illustrates facilitating selection and fulfillment of oneof the bids.

In an embodiment, a request is sent to solicit bids for the one or moreagricultural products or services;

In an embodiment, at least a first bid is received for a first productor service and a second bid for a second product or service; and

In an embodiment, the first or second bid is selected based at least inpart on a selection associated with the plant growth operation.

In an embodiment, the request to solicit offers includes at least a partof the sensor data.

Conclusion

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A system, comprising: one or more processors;memory communicatively coupled to the one or more processors, the memorystoring computer-readable instructions that are executable by the one ormore processors to cause the system to: receive sensor data from one ormore sensors configured to capture data for plants within a plant growthoperation; access accumulated data associated with other plants in otherplant growth operations; analyze the sensor data and accumulated data todetermine one or more conditions of the plants within the plant growthoperation; based on the analysis, determine one or more plant groweractions to improve plant growth; transmit data to a controller deviceassociated with the plant growth operation, the data includinginstructions associated with the one or more plant grower actions;determine one or more agricultural products or services associated withthe plant grower actions; input the determined products or services toan agricultural exchange service configured to process electroniccommerce information from servicers of the products or services;receive, via a user interface, one or more bids from the servicers ofthe products or services, wherein the bids comprise proposals forproviding the products or services; and facilitate selection andfulfillment of one of the bids to a recipient of the data.
 2. The systemof claim 1, further comprising computer-readable instructions that areexecutable by the one or more processors to cause the system to executea closed loop function configured to generate likely candidates fordiagnoses and recommendation.
 3. The system of claim 1, furthercomprising computer-readable instructions that are executable by the oneor more processors to cause the system to determine measured effects ofproducts or services on plants.
 4. The system of claim 1, furthercomprising computer-readable instructions that are executable by the oneor more processors to cause the system to provide a grading or qualityassessment of the agricultural products or services for an issueidentified by an agricultural machine learning model.
 5. The system ofclaim 1, wherein the sensor data includes image data that ishigh-fidelity data that is captured continuously, based on a time lapsesequence, or based on a triggering event.
 6. The system of claim 5,wherein the triggering event may include a change in a relative positionof an individual plant with its surrounds, based on an analysis oftemporally sequential image data, or an indication that an environmentaldata-point has fallen below a predetermined threshold.
 7. The system ofclaim 1, wherein the one or more sensors include environmental sensorsor image capturing devices, wherein the environmental sensors includingat least one of range-finding sensors, light intensity sensors, lightspectrum sensors, non-contact infra-red temperature sensors, thermalsensors, photoelectric sensors that detect changes in color, carbondioxide uptake sensors, water, pH testing, and oxygen productionsensors.
 8. The system of claim 7, wherein the image capturing devicesare capable of capturing hyperspectral images, 3D measurements, RGB,monochrome, or thermal images.
 9. The system of claim 1, wherein theservicers of the products or services include one or more ofagricultural producers, agricultural distributors, or PCA/agronomists.10. The system of claim 1, wherein the agricultural exchange service isfurther configured to transmit information pertaining to the one or moreagricultural products or services associated with the plant groweractions to subscribers of the agricultural exchange service and to aninterface for users of the agricultural exchange service.
 11. The systemof claim 1, further comprising computer-readable instructions that areexecutable by the one or more processors to cause the system to receive,via the user interface, data indicative of a selection of the one ormore bids.
 12. The system of claim 1, wherein: the agricultural exchangeservice is further configured to receive, via the interface, additionalinformation pertaining to the plant growth operation; and the one ormore plant grower actions are further determined based on the additionalinformation.
 13. One or more non-transitory computer-readable mediastoring computer-executable instructions, that when executed on one ormore processors of a computing device, cause the computing device toperform operations comprising: receiving sensor data from one or moresensors configured to monitor plants within a plant growth operation;accessing accumulated data associated with other plants in other plantgrowth operations; analyzing the sensor data and accumulated data todetermine one or more conditions of the plants within the plant growthoperation; based on the analysis, determining one or more plant groweractions to improve plant growth; transmitting data to a controllerdevice associated with the plant growth operation, the data includinginstructions associated with the one or more plant grower actions, thedata operable to automatically control the controller device to executethe instructions; receiving additional sensor data from the one or moresensors; analyzing the additional sensor data to determine one or moreconditions of the plants within the plant growth operation; and based onthe analysis of the additional sensor data, updating the one or moreplant grower actions.
 14. The non-transitory computer-readable media ofclaim 13, wherein the one or more plant grower actions include at leastone of changing a light intensity or a light spectrum of existinglighting within the plant growth operation, changing an amount of wateror a frequency of a watering operation with the plant growth operation,changing an amount of nutrients or fertilizer used with the plant growthoperation, or changing a ratio of nutrients to fertilizer that is usedwithin the plant growth operation.
 15. The non-transitorycomputer-readable media of claim 13, further comprisingcomputer-executable instructions, that when executed on one or moreprocessors of a computing device, cause the computing device to performoperations comprising: determining a progress metric of the plant growthoperation, the progress metric indicative of progress of the plantgrowth operation relative to predicted milestones.
 16. Thenon-transitory computer-readable media of claim 13, further comprisingcomputer-executable instructions, that when executed on one or moreprocessors of a computing device, cause the computing device to performoperations comprising: determining that the controller device isconfigured to automate at least one plant grower action of the one ormore plant grower actions, wherein the data transmitted to thecontroller devices includes computational instructions that cause thecontroller device to automate the at least one plant grower actionwithin the plant growth operation.
 17. The non-transitorycomputer-readable media of claim 13, further comprisingcomputer-executable instructions, that when executed on one or moreprocessors of a computing device, cause the computing device to performoperations comprising: transmitting user data to a user deviceassociated with a plant grower of the plant growth operation, the userdata indicative of the one or more plant grower actions.
 18. Thenon-transitory computer-readable media of claim 15, wherein the analyzethe additional sensor data comprises determining that at least one plantis experiencing a less than optimal plant growth, based at least in parton the progress metric; wherein the update the one or more plant groweractions comprises generating one or more actions to optimize plantgrowth of the at least one plant.
 19. A computer-implemented method,comprising: receiving sensor data from one or more sensors configured tocapture data for plants within a plant growth operation; analyzing thesensor data to determine one or more conditions of the plants within theplant growth operation; based on the analysis, determining one or moreplant grower actions to improve plant growth; determining one or moreagricultural products or services associated with the plant groweractions; inputting the determined products or services to anagricultural exchange service configured to process electronic commerceinformation from servicers of the products or services; receiving, via auser interface, one or more bids from the servicers of the products orservices; and facilitating selection and fulfillment of one of the bids.20. The computer-implemented method of claim 19, further comprising:sending a request to solicit bids for the one or more agriculturalproducts or services; receiving at least a first bid for a first productor service and a second bid for a second product or service; andselecting the first or second bid based at least in part on a selectionassociated with the plant growth operation; wherein the request tosolicit offers includes at least a part of the sensor data.